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Zaunseder E, Mütze U, Okun JG, Hoffmann GF, Kölker S, Heuveline V, Thiele I. Personalized metabolic whole-body models for newborns and infants predict growth and biomarkers of inherited metabolic diseases. Cell Metab 2024; 36:1882-1897.e7. [PMID: 38834070 DOI: 10.1016/j.cmet.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 03/13/2024] [Accepted: 05/09/2024] [Indexed: 06/06/2024]
Abstract
Comprehensive whole-body models (WBMs) accounting for organ-specific dynamics have been developed to simulate adult metabolism, but such models do not exist for infants. Here, we present a resource of 360 organ-resolved, sex-specific models of newborn and infant metabolism (infant-WBMs) spanning the first 180 days of life. These infant-WBMs were parameterized to represent the distinct metabolic characteristics of newborns and infants, including nutrition, energy requirements, and thermoregulation. We demonstrate that the predicted infant growth was consistent with the recommendation by the World Health Organization. We assessed the infant-WBMs' reliability and capabilities for personalization by simulating 10,000 newborns based on their blood metabolome and birth weight. Furthermore, the infant-WBMs accurately predicted changes in known biomarkers over time and metabolic responses to treatment strategies for inherited metabolic diseases. The infant-WBM resource holds promise for personalized medicine, as the infant-WBMs could be a first step to digital metabolic twins for newborn and infant metabolism.
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Affiliation(s)
- Elaine Zaunseder
- School of Medicine, University of Galway, Galway, Ireland; Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany; Data Mining and Uncertainty Quantification (DMQ), Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Ulrike Mütze
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Jürgen G Okun
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Georg F Hoffmann
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Stefan Kölker
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Vincent Heuveline
- School of Medicine, University of Galway, Galway, Ireland; Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
| | - Ines Thiele
- School of Medicine, University of Galway, Galway, Ireland; Discipline of Microbiology, University of Galway, Galway, Ireland; Digital Metabolic Twin Centre, University of Galway, Ireland; Ryan Institute, University of Galway, Galway, Ireland; APC Microbiome Ireland, Cork, Ireland.
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Vera-Siguenza E, Escribano-Gonzalez C, Serrano-Gonzalo I, Eskla KL, Spill F, Tennant D. Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model. PLoS Comput Biol 2023; 19:e1011374. [PMID: 37713666 PMCID: PMC10503963 DOI: 10.1371/journal.pcbi.1011374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 07/19/2023] [Indexed: 09/17/2023] Open
Abstract
It is increasingly apparent that cancer cells, in addition to remodelling their metabolism to survive and proliferate, adapt and manipulate the metabolism of other cells. This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising in-vitro mono-culture models could prove to be limited for uncovering novel metabolic targets able to translate into clinical therapies. Although this is increasingly recognised, and work towards addressing the issue is becoming routinary much remains poorly understood. For instance, knowledge regarding the biochemical mechanisms through which cancer cells manipulate non-cancerous cell metabolism, and the subsequent impact on their survival and proliferation remains limited. Additionally, the variations in these processes across different cancer types and progression stages, and their implications for therapy, also remain largely unexplored. This study employs an interdisciplinary approach that leverages the predictive power of mathematical modelling to enrich experimental findings. We develop a functional multicellular in-silico model that facilitates the qualitative and quantitative analysis of the metabolic network spawned by an in-vitro co-culture model of bone marrow mesenchymal stem- and myeloma cell lines. To procure this model, we devised a bespoke human genome constraint-based reconstruction workflow that combines aspects from the legacy mCADRE & Metabotools algorithms, the novel redHuman algorithm, along with 13C-metabolic flux analysis. Our workflow transforms the latest human metabolic network matrix (Recon3D) into two cell-specific models coupled with a metabolic network spanning a shared growth medium. When cross-validating our in-silico model against the in-vitro model, we found that the in-silico model successfully reproduces vital metabolic behaviours of its in-vitro counterpart; results include cell growth predictions, respiration rates, as well as support for observations which suggest cross-shuttling of redox-active metabolites between cells.
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Affiliation(s)
- Elias Vera-Siguenza
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- Watson School of Mathematics, University of Birmingham, Birmingham, United Kingdom
| | - Cristina Escribano-Gonzalez
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Irene Serrano-Gonzalo
- Instituto de Investigación Sanitaria Aragón, Fundación Española para el Estudio y Terapéutica de la enfermedad de Gaucher y otras Lisosomales, Zaragoza, España
| | - Kattri-Liis Eskla
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Fabian Spill
- Watson School of Mathematics, University of Birmingham, Birmingham, United Kingdom
| | - Daniel Tennant
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
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Lüleci HB, Uzuner D, Çakır T, Thambisetty M. Computational Approaches to Assess Abnormal Metabolism in Alzheimer's Disease Using Transcriptomics. Methods Mol Biol 2023; 2561:173-189. [PMID: 36399270 DOI: 10.1007/978-1-0716-2655-9_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Transcriptome-integrated human genome-scale metabolic models (GEMs) have been used widely to assess alterations in metabolism in response to disease. Transcriptome integration leads to identification of metabolic reactions that are differentially inactivated in the tissue of interest. Among the methods available for mapping transcriptome data on GEMs, we focus here on an Integrative Metabolic Analysis Tool (iMAT), which we have recently applied to the analysis of Alzheimer's disease (AD). We provide a detailed protocol for applying iMAT to create models of personalized metabolic networks, which can be further processed to identify reactions associated with abnormal metabolism.
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Affiliation(s)
- Hatice Büşra Lüleci
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey
| | - Dilara Uzuner
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey
| | - Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey
| | - Madhav Thambisetty
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, MD, USA.
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Beura S, Kundu P, Das AK, Ghosh A. Metagenome-scale community metabolic modelling for understanding the role of gut microbiota in human health. Comput Biol Med 2022; 149:105997. [DOI: 10.1016/j.compbiomed.2022.105997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/03/2022] [Accepted: 08/14/2022] [Indexed: 11/03/2022]
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Zhao Y, Zhong X, Yan J, Sun C, Zhao X, Wang X. Potential roles of gut microbes in biotransformation of natural products: An overview. Front Microbiol 2022; 13:956378. [PMID: 36246222 PMCID: PMC9560768 DOI: 10.3389/fmicb.2022.956378] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 08/29/2022] [Indexed: 11/23/2022] Open
Abstract
Natural products have been extensively applied in clinical practice, characterized by multi-component and multi-target, many pharmacodynamic substances, complex action mechanisms, and various physiological activities. For the oral administration of natural products, the gut microbiota and clinical efficacy are closely related, but this relationship remains unclear. Gut microbes play an important role in the transformation and utilization of natural products caused by the diversity of enzyme systems. Effective components such as flavonoids, alkaloids, lignans, and phenols cannot be metabolized directly through human digestive enzymes but can be transformed by enzymes produced by gut microorganisms and then utilized. Therefore, the focus is paid to the metabolism of natural products through the gut microbiota. In the present study, we systematically reviewed the studies about gut microbiota and their effect on the biotransformation of various components of natural products and highlighted the involved common bacteria, reaction types, pharmacological actions, and research methods. This study aims to provide theoretical support for the clinical application in the prevention and treatment of diseases and provide new ideas for studying natural products based on gut biotransformation.
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Affiliation(s)
- Yucui Zhao
- Ministry of Education Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xinqin Zhong
- Ministry of Education Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Junyuan Yan
- Ministry of Education Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Congying Sun
- Ministry of Education Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xin Zhao
- Ministry of Education Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- *Correspondence: Xin Zhao,
| | - Xiaoying Wang
- Ministry of Education Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Xiaoying Wang,
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Ng RH, Lee JW, Baloni P, Diener C, Heath JR, Su Y. Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer. Front Oncol 2022; 12:914594. [PMID: 35875150 PMCID: PMC9303011 DOI: 10.3389/fonc.2022.914594] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 05/30/2022] [Indexed: 11/13/2022] Open
Abstract
The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets.
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Affiliation(s)
- Rachel H. Ng
- Institute for Systems Biology, Seattle, WA, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
| | - Jihoon W. Lee
- Medical Scientist Training Program, University of Washington, Seattle, WA, United States
- Program in Immunology, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | | | | | - James R. Heath
- Institute for Systems Biology, Seattle, WA, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
- *Correspondence: James R. Heath, ; Yapeng Su,
| | - Yapeng Su
- Program in Immunology, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
- Herbold Computational Biology Program, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
- *Correspondence: James R. Heath, ; Yapeng Su,
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Hrovatin K, Fischer DS, Theis FJ. Toward modeling metabolic state from single-cell transcriptomics. Mol Metab 2022; 57:101396. [PMID: 34785394 PMCID: PMC8829761 DOI: 10.1016/j.molmet.2021.101396] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 10/21/2021] [Accepted: 11/09/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Single-cell metabolic studies bring new insights into cellular function, which can often not be captured on other omics layers. Metabolic information has wide applicability, such as for the study of cellular heterogeneity or for the understanding of drug mechanisms and biomarker development. However, metabolic measurements on single-cell level are limited by insufficient scalability and sensitivity, as well as resource intensiveness, and are currently not possible in parallel with measuring transcript state, commonly used to identify cell types. Nevertheless, because omics layers are strongly intertwined, it is possible to make metabolic predictions based on measured data of more easily measurable omics layers together with prior metabolic network knowledge. SCOPE OF REVIEW We summarize the current state of single-cell metabolic measurement and modeling approaches, motivating the use of computational techniques. We review three main classes of computational methods used for prediction of single-cell metabolism: pathway-level analysis, constraint-based modeling, and kinetic modeling. We describe the unique challenges arising when transitioning from bulk to single-cell modeling. Finally, we propose potential model extensions and computational methods that could be leveraged to achieve these goals. MAJOR CONCLUSIONS Single-cell metabolic modeling is a rising field that provides a new perspective for understanding cellular functions. The presented modeling approaches vary in terms of input requirements and assumptions, scalability, modeled metabolic layers, and newly gained insights. We believe that the use of prior metabolic knowledge will lead to more robust predictions and will pave the way for mechanistic and interpretable machine-learning models.
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Affiliation(s)
- Karin Hrovatin
- Institute of Computational Biology, Helmholtz Center Munich, Ingolstaedter Landstraße 1, Neuherberg, 85764, Germany; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Alte Akademie 8, Freising, 85354, Germany.
| | - David S Fischer
- Institute of Computational Biology, Helmholtz Center Munich, Ingolstaedter Landstraße 1, Neuherberg, 85764, Germany; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Alte Akademie 8, Freising, 85354, Germany.
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Ingolstaedter Landstraße 1, Neuherberg, 85764, Germany; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Alte Akademie 8, Freising, 85354, Germany; Department of Mathematics, Technical University of Munich, Boltzmannstr. 3, Garching bei München, 85748, Germany.
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8
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Chung CH, Lin DW, Eames A, Chandrasekaran S. Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms. Metabolites 2021; 11:606. [PMID: 34564422 PMCID: PMC8470976 DOI: 10.3390/metabo11090606] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 09/01/2021] [Accepted: 09/03/2021] [Indexed: 12/18/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein-protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms.
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Affiliation(s)
- Carolina H. Chung
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (C.H.C.); (A.E.)
| | - Da-Wei Lin
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Alec Eames
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (C.H.C.); (A.E.)
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (C.H.C.); (A.E.)
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA;
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Bioinformatics and Computational Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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Editorial overview: Tissue, cell and pathway engineering: programming biology for smart therapeutics, microbial cell factory and intelligent biomanufacturing. Curr Opin Biotechnol 2020; 66:iii-vi. [PMID: 33218951 DOI: 10.1016/j.copbio.2020.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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